Порівняння методів

	Аналіз різноманітності мікробіому в часових рядах	Диференційна експресія генів у часових рядах РНК-сек
Галузь	Біоінформатика	Біоінформатика
Родина	Process / pipeline	Process / pipeline
Рік появи	2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020)	2006–2018 (principal methods established)
Автор методу	Developed iteratively through the microbiome research community; key contributions from Susan Holmes, Rob Knight, and colleagues	Conesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others
Тип	Longitudinal observational / bioinformatics pipeline	Computational genomics pipeline
Основоположне джерело	Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583.	Conesa, A., Nueda, M. J., Ferrer, A., & Talon, M. (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics, 22(9), 1096–1102.
Інші назви	longitudinal microbiome diversity analysis, temporal microbiome analysis, repeated-measures microbiome diversity, time-course microbiome analysis	longitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis
Пов'язані	5	6
Підсумок	Time-series microbiome diversity analysis tracks how the richness, evenness, and community composition of microbial communities change across multiple time points within the same subjects. By combining standard diversity metrics with longitudinal statistical models, it separates true temporal dynamics from inter-individual variation, identifying when and how perturbations such as diet changes, antibiotic treatment, or disease onset reshape the microbiome.	Time-series RNA-seq differential expression analysis identifies genes whose expression levels change systematically across ordered time points — such as during development, disease progression, or response to a treatment. Unlike two-condition DE analysis, it explicitly models the temporal structure of the data, capturing dynamic gene expression trajectories rather than a single snapshot contrast. Tools such as maSigPro, ImpulseDE2, and splineTimeR have been developed specifically for this design.